149 citations · 200 across the 9 of their papers we have counts for
9 papers
TRUST-FS: Tensorized Reliable Unsupervised Multi-View Feature Selection for Incomplete Data
Minghui Lu, Yanyong Huang, Minbo Ma +4
Multi-view unsupervised feature selection (MUFS), which selects informative features from multi-view unlabeled data, has attracted increasing research interest in recent years. Alt…
GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values
Songyu Ke, Chenyu Wu, Yuxuan Liang +3
The ubiquity of missing data in urban intelligence systems, attributable to adverse environmental conditions and equipment failures, poses a significant challenge to the efficacy o…
CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection
Yanyong Huang, Yuxin Cai, Dongjie Wang +2
The objective of multi-view unsupervised feature and instance co-selection is to simultaneously iden-tify the most representative features and samples from multi-view unlabeled dat…
Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection
Li Yang, Yanyong Huang, Dongjie Wang +4
Semi-supervised multi-label feature selection has recently been developed to solve the curse of dimensionality problem in high-dimensional multi-label data with certain samples mis…
CIMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection
Yanyong Huang, Zongxin Shen, Yuxin Cai +4
Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods ass…
Incremental Unsupervised Feature Selection for Dynamic Incomplete Multi-view Data
Yanyong Huang, Kejun Guo, Xiuwen Yi +2
Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods ass…
Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks
Junkai Sun, Junbo Zhang, Qiaofei Li +3
Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public…
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting
Zheyi Pan, Yuxuan Liang, Junbo Zhang +3
Spatio-temporal (ST) data, which represent multiple time series data corresponding to different spatial locations, are ubiquitous in real-world dynamic systems, such as air quality…
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks
Junbo Zhang, Yu Zheng, Dekang Qi +3
Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dep…